4.8 Article

MetazExp: a database for gene expression and alternative splicing profiles and their analyses based on 53 615 public RNA-seq samples in 72 metazoan species

期刊

NUCLEIC ACIDS RESEARCH
卷 50, 期 D1, 页码 D1046-D1054

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OXFORD UNIV PRESS
DOI: 10.1093/nar/gkab933

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资金

  1. Fundamental Research Funds for the Central Universities [KYXK2021006]
  2. USDA Hatch Project [MICL02560]
  3. Michigan State University

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MetazExp is a database for gene expression and alternative splicing profiles based on 53,615 uniformly processed publicly available RNA-seq samples from 72 metazoan species. Users can conveniently query gene expression and alternative splicing profiles by gene IDs, symbols, functional terms, and sequence similarity, and flexibly customize experimental groups for differential and specific analyses. With data visualization tools and comprehensive links with external databases, users can efficiently explore results and gain insights.
RNA-seq has been widely used in experimental studies and produced a massive amount of data deposited in public databases. New biological insights can be obtained by retrospective analyses of previously published data. However, the barrier to efficiently utilize these data remains high, especially for those who lack bioinformatics skills and computational resources. We present MetazExp (https://bioinfo.njau.edu.cn/metazExp), a database for gene expression and alternative splicing profiles based on 53 615 uniformly processed publicly available RNA-seq samples from 72 metazoan species. The gene expression and alternative splicing profiles can be conveniently queried by gene IDs, symbols, functional terms and sequence similarity. Users can flexibly customize experimental groups to perform differential and specific expression and alternative splicing analyses. A suite of data visualization tools and comprehensive links with external databases allow users to efficiently explore the results and gain insights. In conclusion, MetazExp is a valuable resource for the research community to efficiently utilize the vast public RNA-seq datasets.

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